predicting effective adaptation to breast cancer to help women to bounce back

Conditions: Breast Cancer

Summary: breast cancer accounts for 28% of all cancer cases in europe ("who," 2018). coping with breast cancer is becoming an increasingly burdensome socio-economic challenge, partly due to the increasing incidence registered in the last years, which is occurring despite continuous advances in medicine. this said, mortality rate has decreased significantly as 5-year survival rate has risen from 75% to 90% ("breast cancer research foundation," 2016). for these reasons the number of cancer survivors has grown, with important effects on quality of life even years after the end of treatments. the process of successful adaptation to breast cancer and the various accompanying stressors can be conceptually defined as the person's resilience. resilience is a complex construct that can be defined on different levels: an individual's potential (capacity to engage in adaptive coping processes), a process (adaptive reactions to adversity), and an outcome (the final state achieved as the result of coping). while theoretical contributions regarding resilience models in medical settings have been advanced (deshields et al., 2016), to date no one has tested the integrated contributing role of multiple psychological, biological and functional variables in predicting the patient's ability to bounce back from the stressful life event of being diagnosed with breast cancer. resilience is going to be measured through a data-driven method (computation of resilience index on the basis of retrospective data) and through a psychometric method (various domains of resilience through questionnaires). there is a growing need for novel strategies to improve the understanding and the capacity to predict resilience of women to the variety of stressful experiences. bounce european project (h2020 european project "predicting effective adaptation to breast cancer to help women to bounce back"; grant agreement nr: 777167) will bring together modelling, medical, and social sciences experts to advance current knowledge on the dynamic nature of resilience as it relates to improved quality of life. the broad and general objective of the bounce project is to build a quantitative mathematical model of factors associated with optimal adjustment capacity to cancer. the investigators will collect biomedical status (bms), the psychosocial status (pss) and the functional status (fus) of breast cancer patients that literature demonstrated to be predictive of patients' capacity to bounce back during the highly stressful treatment and recovery period following diagnosis of breast cancer. the overarching goal of the model is to understand which factors predict optimal adjustment to breast cancer and which combination of factors undermine adjustment. this would allow early identification of women at risk for which it is better to intervene through a personalised psychological support. funding: the bounce project "predicting effective adaptation to breast cancer to help women to bounce back" has received funding from the european union's horizon 2020 research and innovation programme, project type: h2020 - sci-2017-cnect-2; project type: h2020 - sci-2017-cnect-2; grant agreement nr: 777167.


Eligibility criteria:
inclusion criteria: * presence of a devoted informed consent signed by the patient and the physician * histologically confirmed invasive early or locally advanced operable breast cancer * tumour stage i, ii and iii * patients receiving surgery as part of the local treatment * patients receiving any type of systemic treatment regardless of treatment type and/or adjuvant radiation therapy if indicated as part of the local treatment exclusion criteria: * refusal to sign informed consent * presence of distant metastases * history of another malignancy or contralateral invasive breast cancer within the last five years except cured basal cell carcinoma of skin or carcinoma in situ of the uterine cervix * history of early onset (i.e., before 40 years of age) mental disorder (i.e., schizophrenia, psychosis, bipolar disorder, diagnosis of major depression) or severe neurologic disorder (i.e., neurodegenerative disorder, dementia) * serious other diagnosed concomitant diseases such as clinically significant (i.e. active) cardiac disease (e.g. congestive heart failure, symptomatic coronary artery disease or cardiac arrhythmia not well controlled with medication) or myocardial infarction within the last 12 months. * major surgery for a severe disease or trauma which could affect patient's psychosocial wellbeing (for example, major heart or abdominal surgery) within 4 weeks prior to study entry or lack of complete recovery from the effects of surgery * treatment for invasive cancer * treatment for any major illness in the last half year * pregnancy or breastfeeding at time of recruitment

Keywords: resilience predictive model